Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable precise assessment and can significantly speed up change detection. Aerial imagery usually provides images with much higher pixel resolution than satellite data allowing more detailed mapping. However, there is still a lack of aerial datasets made for the segmentation, covering rural areas with a resolution of tens centimeters per pixel, manual fine labels, and highly publicly important environmental instances like buildings, woods, water, or roads. Here we introduce LandCover.ai (Land Cover from Aerial Imagery) dataset for semantic segmentation. We collected images of 216.27 sq. km rural areas across Poland, a country in Central Europe, 39.51 sq. km with resolution 50 cm per pixel and 176.76 sq. km with resolution 25 cm per pixel and manually fine annotated four following classes of objects: buildings, woodlands, water, and roads. Additionally, we report simple benchmark results, achieving 85.56% of mean intersection over union on the test set. It proves that the automatic mapping of land cover is possible with a relatively small, cost-efficient, RGB-only dataset. The dataset is publicly available at https://landcover.ai.linuxpolska.com/
翻译:对土地覆盖和土地使用的监测对自然资源管理至关重要。自动视觉制图可以给农业、林业或公共行政带来巨大的经济价值。卫星或航空图像,加上计算机视野和深学习,可以进行精确的评估,并能够大大加快变化探测。空中图像通常提供比卫星数据更先进的像素分辨率图像,以便进行更详细的测绘。然而,仍然缺乏用于分割的航空数据集,覆盖农村地区,每像素有数十厘米的分辨率,人工细标签,以及建筑、林木、水或道路等非常重要的公开环境实例。这里我们介绍LandCover.ai(空中图像覆盖)数据集,以便进行语义分割。我们收集波兰(中欧国家)216.27平方公里农村地区图像,每像素分辨率为39.51平方公里,每像素有176.76平方公里的分辨率,每像素有25厘米的分辨率,手动精细标签,并有以下四类物体:建筑物、林地、水和道路等。此外,我们报告简单的基准结果,实现85.56%的空中图像覆盖。我们收集了波兰(中欧国家)2,39.51平方公里的图像的图像的图像,这是在公开测试中可能得到的交叉数据。